SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9326–9350 of 10420 papers

TitleStatusHype
Knowledge Distillation by On-the-Fly Native EnsembleCode0
Dual Pattern Learning Networks by Empirical Dual Prediction Risk Minimization—0
Improving Whole Slide Segmentation Through Visual Context - A Systematic Study—0
A Systematic Evaluation of Recent Deep Learning Architectures for Fine-Grained Vehicle ClassificationCode0
Revisiting Adversarial Risk—0
Dimensionality-Driven Learning with Noisy LabelsCode0
Path-Level Network Transformation for Efficient Architecture SearchCode0
Training Faster by Separating Modes of Variation in Batch-normalized ModelsCode0
Exploring Feature Reuse in DenseNet Architectures—0
Deep Gaussian Processes with Convolutional Kernels—0
Factorized Adversarial Networks for Unsupervised Domain Adaptation—0
Meta-Learner with Linear Nulling—0
Between Progress and Potential Impact of AI: the Neglected Dimensions—0
A Novel Framework for Recurrent Neural Networks with Enhancing Information Processing and Transmission between Units—0
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks—0
Webly Supervised Learning Meets Zero-Shot Learning: A Hybrid Approach for Fine-Grained Classification—0
The Power of Ensembles for Active Learning in Image Classification—0
Tandem Blocks in Deep Convolutional Neural Networks—0
A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation—0
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning—0
Analyzing Filters Toward Efficient ConvNet—0
Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input—0
Classification-Driven Dynamic Image Enhancement—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Coupled End-to-End Transfer Learning With Generalized Fisher Information—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified